<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Multivariate beta regression models for jointly modelling two or more variables whose values belong in the (0,1) interval, such as indexes, rates or proportions, are proposed for making small area predictions. The multivariate model can help the estimation process by borrowing strength between units and obtaining more precise estimates, especially for small samples. Each response variable is assumed to have a beta distribution so the models could accommodate multivariate asymmetric data. Copula functions are used to construct the joint distribution of the dependent variables; all the marginal distributions are fixed as beta. A hierarchical beta regression model is additionally proposed with correlated random effects. We present an illustration of the proposed approach by estimating two indexes of educational attainment at school level in a Brazilian state. Our predictions are compared with separate univariate beta regressions. The inference process was conducted using a full Bayesian approach.</p></abstract>

Multivariate beta regression models for jointly modelling two or more variables whose values belong in the (0,1) interval, such as indexes, rates or proportions, are proposed for making small area predictions. The multivariate model can help the estimation process by borrowing strength between units and obtaining more precise estimates, especially for small samples. Each response variable is assumed to have a beta distribution so the models could accommodate multivariate asymmetric data. Copula functions are used to construct the joint distribution of the dependent variables; all the marginal distributions are fixed as beta. A hierarchical beta regression model is additionally proposed with correlated random effects. We present an illustration of the proposed approach by estimating two indexes of educational attainment at school level in a Brazilian state. Our predictions are compared with separate univariate beta regressions. The inference process was conducted using a full Bayesian approach.

Multivariate Beta Regression with Application in Small Area Estimation

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Multivariate Beta Regression with Application in Small Area Estimation"}

Multivariate beta regression models for jointly modelling two or more variables whose values belong in the (0,1) interval, such as indexes, rates or...

Multivariate Beta Regression with Application in Small Area Estimation

Online veröffentlicht: 23. Sept. 2016

Seitenbereich: 747 - 768

Eingereicht: 01. Feb. 2015

Akzeptiert: 01. Feb. 2016

DOI: https://doi.org/10.1515/jos-2016-0038

Schlüsselwörter
Bayesian inference, copula function, small domain, education evaluation

© 2016 Debora F. Souza et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Multivariate Beta Regression with Application in Small Area Estimation

Online veröffentlicht: 23. Sept. 2016

Seitenbereich: 747 - 768

Eingereicht: 01. Feb. 2015

Akzeptiert: 01. Feb. 2016

DOI: https://doi.org/10.1515/jos-2016-0038

SchlüsselwörterBayesian inference, copula function, small domain, education evaluation

© 2016 Debora F. Souza et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Bayesian inference, copula function, small domain, education evaluation